More and more systems, whether these are security systems or road charging systems, rely on the taking of photographs in order to identify people or vehicles. Information is extracted from the photograph, such as a vehicle registration number or an employee number, in order to identify the person or vehicle in the photograph.
When relying on these photographs for identification or data extraction purposes, the quality of the photograph plays a vital part. There are many variables that can hinder the taking of a good photograph. Often the weather obscures the image in a photograph. For example, the weather may be sunny and bright and although this would seem good weather conditions for taking a photograph, the sun may reflect on the vehicle's paint work and cause a certain amount of glare, thus causing distortion of the image in the photograph. On another day it may be snowing and thus a clear image cannot be taken because the snow is adhering to the vehicle thus obscuring the vehicle registration number. The availability of an adequate light source in which to illuminate the vehicle's registration number has an impact on whether a clear image of the vehicle's registration number can be taken. Other facts may include how fast the vehicle is travelling, the vehicle's height and size, etc. The fact that the characters that make up the vehicle licence plate have been manipulated and therefore display an invalid vehicle registration number or the angle of the camera may be positioned too narrowly in relation to the position of the vehicle's registration number and therefore the resulting photograph is of poor quality.
In order to use information that is contained within a photograph, optical character recognition (OCR) systems are deployed to translate characters within the image into a standard encoding scheme. The translated characters can then be processed by a computer program to perform a data look up operation against, for example, a vehicle registration number database, in order to locate the registered owner of a vehicle and to interface with a charging system in order to charge the registered owner of the vehicle a sum of money for travelling through the charging point area. Problems arise when a photograph taken of a vehicle registration number plate is not a true and accurate representation of the vehicle registration number due to a camera taking a poor quality photograph.
In order to process a photograph in order to extract information from it, an OCR system processes a photograph by translating each character identified in the photograph into a series of computer readable characters. The OCR translates the characters identified in the images according to a defined format. For example, a predefined format may state that for all vehicles having a GB vehicle registration number, the format is two alphabetic characters, followed by two numerical characters, followed by three alphabetic characters. However, when an OCR device is translating characters identified in the image and the image is of poor quality, the OCR device has to ‘guess’ what a character might be. For example, is the alphanumeric character an ‘I’ or an ‘1’ etc. Often, around five to twenty percent of characters are misrecognised, which leads to the incorrect registered owners being charged or some registered owners not being charged at all. This problem is also compounded, in a congestion charging environment, by the fact that some vehicles will drive through a charging point several times a day. Often, when a charging system is unable to locate the registered owner of a vehicle, the charging system will send the translated OCR identification along with the photograph that the translation comes from to a manual agent for identification. Manual agents, through their own experience are then able to interpret the OCR translation for sending back to the charging system.
Thus there is a need for a method and an apparatus in which erroneous OCR identifications generated by OCR devices can be corrected.